code: def test_score(xy_classification):
      distributed = pytest.importorskip("distributed")
      client = distributed.Client(n_workers=2)
  
      X, y = xy_classification
      inc = Incremental(
          SGDClassifier(max_iter=1000, random_state=0, tol=1e-3),
          scoring="accuracy",
      )
  
      with client:
          inc.fit(X, y, classes=[0, 1])
          result = inc.score(X, y)
          expected = inc.estimator_.score(*dask.compute(X, y))
  
      assert result == expected
  
imports: import dask
  import dask.array
  import dask.dataframe
  import numpy
  import pandas
  import pytest
  import sklearn.datasets
  import sklearn.model_selection
  from dask.array.utils import assert_eq
  from scipy.sparse import csr_matrix
  from sklearn.base import clone
  from sklearn.linear_model import SGDClassifier, SGDRegressor
  from sklearn.pipeline import make_pipeline
  import dask_ml.feature_extraction.text
  import dask_ml.metrics
  from dask_ml.metrics.scorer import check_scoring
  from dask_ml.wrappers import Incremental
label_desc: Use image node_id 9 for calling a global function with example usage: test_score(xy_classification) without return types